Presented by: Dardan Xhymshiti Fall 2015

 Authors: Eli Cortez, Philip A.Bernstein, Yeye He, Lev Novik (Microsoft Corporation)  Conference: VLDB  Type: Demonstration

 Data discovery of relevant information in relational databases.  Problem of generating reports.  To find relevant information, users have to find the database tables that are relevant to the task, for each of them understand its content to determine whether it is truly relevant etc.  The schema’s table and column names are often not very descriptive of the content.  Example: In 412 data columns in 639 tables from 29 databases used by Microsoft’s IT organization, 28% of all columns were very generic as: name, it, description, field, code

 A typical corporate database table with generic column names

 Such non-descriptive column names make it difficult to search and understand the table.  One solution: using data stewards to enrich the database tables and columns with textual description.  Time consuming  Ignoring databases that are less frequent.

 Barcelos: automatically annotate columns of database tables.  Annotate even those tables that are not frequent.  How it works?  It works by mining spreadsheets.  Many of these spreadsheets are generated by queries.  It uses spreadsheet’s column names as candidate annotations for the corresponding database columns.  For the table above Barcelos produces annotations:  TeamID and Team for the first column,  Delivery Team and Team for the second column.  Line of Business and Business for the third.

 The authors have provided a method to for extracting relevant tables from an enterprise database.  A method for identifying and ranking relevant column annotations.  An implementation of Barcelos and an experimental evaluation that shows its efficiency and effectiveness.